
and \(\partial x_i / \partial x\)  is defined as long as the random sampling is differentiable w.r.t. the input image \(x\)  which is often the case of data augmentations.
This framing of text-driven generation has been applied to different generators \(G\) , yielding a variety of artistic results: using unconditional GAN generation, like BigGAN [1]}, VQGAN [2]} and SIREN [3]};
conditional generation using GAN, such as StyleCLIP [4]}, that enables editing existing images.
In addition to GANs, it can also be applied to Diffusion models [5]}, [6]}, [7]}.
